measure_variance_explained

a <code>partition_step</code> object

.partition_step

Metrics are functions that tell how much information would be
lost for a given reduction in the data. reduce. <code><a rd-options="=as_measure" href="/link/as_measure()?package=partition&version=0.1.0&to=%3Das_measure" data-mini-rdoc="=as_measure::as_measure()">as_measure()</a></code> is a
helper function to create new metrics to be used in <code>partitioner</code>s.
<code>partitioner</code>s can be created with <code><a rd-options="=as_partitioner" href="/link/as_partitioner()?package=partition&version=0.1.0&to=%3Das_partitioner" data-mini-rdoc="=as_partitioner::as_partitioner()">as_partitioner()</a></code>.<code>measure_variance_explained()</code> assesses information loss by calculating the
variance explained by the first component of a principal components analysis.

A fast and flexible framework for agglomerative partitioning. 'partition' uses an approach
called Direct-Measure-Reduce to create new variables that maintain the
user-specified minimum level of information. Each reduced variable is also interpretable:
the original variables map to one and only one variable in the reduced data set. 'partition'
is flexible, as well: how variables are selected to reduce, how information loss is measured,
and the way data is reduced can all be customized.

Malcolm Barrett

measure_variance_explained: Measure the information loss of reduction using the variance explained

Description

Usage

Arguments

Value

See Also